Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Authors: Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Numerical Experiments We use the same dataset as (Zhang & Zhu, 2017), i.e., the Adult dataset from the UCI Machine Learning Repository (Lichman, 2013). It consists of personal information of around 48,842 individuals... Figures 2(a)-2(b) show both Lmean(t) and Lrange(t) as vertical bars centered at Lmean(t). Their corresponding privacy upper bound is given in Figures 2(c)-2(d). |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA. |
| Pseudocode | Yes | Algorithm 1 Penalty perturbation (PP) method Parameter: Determine θ such that 2c1 < Bi N + 2θVi) holds for all i. Initialize: Generate fi(0) randomly and λi(0) = 0d 1 for every node i N , t = 0 Input: {Di}N i=1, {αi(1), , αi(T)}N i=1 for t = 0 to T 1 do... |
| Open Source Code | No | The paper does not provide any specific links to source code repositories, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | We use the same dataset as (Zhang & Zhu, 2017), i.e., the Adult dataset from the UCI Machine Learning Repository (Lichman, 2013). |
| Dataset Splits | No | The paper uses the Adult dataset and mentions a "training set" and "test set" implicitly through evaluation metrics, but does not explicitly specify a "validation" set or detailed dataset splits (e.g., percentages or counts) for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU types, or cloud infrastructure details) for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We will use as loss function the logistic loss L (z) = log(1 + exp( z)), with |L | 1 and L c1 = 1 4. The regularizer is R(fi) = 1 2||fi||2 2. ... for simplicity of presentation we shall fix θ = 0.5, let ηi(t) = η(t) = θqt 1 1 , and noise αi(t) = α(t) = α(1)qt 1 2 for all nodes. |